Over the last twenty years or so, big data has seen a noteworthy increase in prevalence. Huge amounts of money are continually pumped into big data due to the successful accomplishments of corporations such as Google, Amazon, and Facebook, which have allowed for immense advancements in large-scale data analysis. As a result of this progress, many enterprises are making data-driven decision-making their highest precedence. 

For the past decade, articles and reports have indicated that AI and big data are the keys to unlocking success in businesses. We have been convinced that these technologies are what will enable modern companies to thrive in a digitally focused, consumer-oriented world. This narrative assert that data processing capabilities and analysis are the only way to solve problems in a variety of industries, including financial services, healthcare, and real estate, with AI and big data at the forefront. 

What is Small Data? 

Small data is composed of small, interoperable, and purpose-specific containers that can exist in a variety of places. Examples include electronic vaccination certificates to gain entry to events, QR codes to view restaurant menus, contactless checkout systems, the Clear Pass at airports, and any item stored on Apple Wallet or Google Pay. It is secured through wrappers that make it uncoupled from centralized networks; this allows for more powerful AI models for developers, better medications for researchers, and more competitive business insights for CEOs. 

A Paradigm Shift with Small Data 

Most enterprise data being unstructured has proven to be a problem for many businesses when it comes to collecting massive amounts of data. It’s become evident that the advantages of big data are only available to those with a giant budget and a digital-first business model, such as tech giants. This isn’t ideal for smaller organizations or SMEs that have minimal datasets yet still require information from them through leveraging machine learning. These companies don’t need huge datasets to benefit; they need solutions that can extract valuable insights from their small ones. 

Small data holds the key to realizing the complete potential of AI by bringing forth the hidden value of data and allowing for more effective business results throughout an organization. By contrast, large amounts of data can impede AI’s potential. 

Applications of Small Data 

The advent of AI tools and techniques, in combination with focusing on human aspects, has enabled the potential to train AI using limited datasets and reformulate processes. In healthcare, for instance, clinicians benefit from small data in terms of providing quick and efficient patient care, as it aids in tactical tasks such as confirming allergies, organizing times for blood cultures, and noting missed appointments. 

Similarly, restaurants and customer-focused businesses can use small data to analyze their short- and long-term potential. Utilizing cloud-based software that includes an up-to-date business dashboard, related modules, and external connections will grant a competitive edge. 

Benefits of Taking the Small Data Approach   


For organizations of all sizes, small data offers distinct advantages over large data. Opposed to traditional applications that use a significant amount of data, small-data applications are produced through collaboration between humans and machines, thus making the results explainable, trustworthy, and transparent. In a competitive world dominated by data-hungry algorithms, mastering the challenges posed by AI in collaboration with small data could provide an advantage over those relying solely on big data solutions. 

Accumulating copious amounts of data without any way to utilize it is an inefficient waste of resources and time. It is time to change the narrative by focusing on a small data approach that can unlock the potential of AI-driven insights while providing value from enterprise data. The key is to make the data useful. 


  1. https://towardsdatascience.com/is-small-data-the-next-big-thing-in-data-science-9acc7f24907f
  2. https://www.forbes.com/sites/forbestechcouncil/2022/03/24/small-data-big-impact-making-the-most-of-ai-with-less/?sh=1abfb2064ff6
  3. https://hbr.org/2020/02/small-data-can-play-a-big-role-in-ai
  4. https://www.analyticsinsight.net/importance-of-small-data-in-machine-learning/
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